A Survey on Various Brain MR Image Segmentation Techniques

Prior to medical image analysis, segmentation is an essential step in the preprocessing process. Partitioning an image into distinct regions based on characteristics like texture, color, and intensity is its primary goal. Numerous applications include tumor and coronary border recognition, surgical planning, tumor volume measurement, blood cell classification and heart image extraction from cardiac cine angiograms are all made possible by this technique. Many segmentation methods have been proposed recently for medical images. Thresholding, region-based, edge-based, clustering-based and fuzzy based methods are the most important segmentation processes in medical image analysis. A variety of image segmentation methods have been developed by researchers for efficient analysis. An overview of widely used image segmentation methods, along with their benefits and drawbacks, is provided in this paper

Image Segmentation Techniques: A Survey

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

Image Segmentation Techniques: A Survey

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels

FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC

Indirect Immunofluorescence (IIF) is a gold standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Automated interpretation is crucial to assure high accuracy to determine the autoantibody type of diseases. There are different classifier algorithm methods that have been proposed in previous works to classify the fluorescence intensity, however, there is still no valid algorithms to set as a standard. The purpose of this study is to classify the fluorescence intensity by using fuzzy logic algorithm to determine the positivity of the Hep2-cell serum samples. The scope of study of this project involves converting the RGB colour space of images to LAB colour space and the mean value of the lightness channel and chromaticity layer (a) channel is extracted and classified by using fuzzy logic algorithm based on the standard score ranges of ANA fluorescence intensity which are 4+, 3+, 2+, 1+ and 0. Based on the results, the accuracy of intermediate and positive class is 85% and 87% respectively